TabNet-SFO: An Intrusion Detection Model for Smart Water Management in Smart Cities

Abstract

As Smart City (SC) infrastructures evolve rapidly, securing critical systems like smart water management (SWM) becomes paramount to protecting against cyber threats. Enhancing the security, sustainability and execution of conventional schemes is considered significant in developing smart environments. Intrusion detection systems (IDS) can be effectively leveraged to realise this security objective in an Internet of Things (IoT)-based smart environment. This research addresses this need by proposing a novel IDS model called TabNet architecture optimised using Sailfish Optimisation (SFO). The TabNet-SFO model was specifically developed for SWM in SC applications. The proposed IDS model includes data collection, preprocessing, feature selection and classification processes. For training the model, this research used the CIC-DDoS-2019 dataset, and for evaluation, real-time data collected using an IoT-based smart water metre are used. The preprocessing step eliminates unnecessary features, cleans the data, encodes labels and normalises the applied datasets. After preprocessing, the TabNet model selects significant features in the dataset. The TabNet architecture was optimised using the SFO algorithm, which allows hyperparameter tuning and model optimisation. The proposed model demonstrated improved detection accuracy and efficiency on both the simulated and real-time datasets. The model attained a 98.90% accuracy, a 98.85% recall, a 98.80% precision, a 98.82% specificity and a 98.78% f1 score on the CIC-DDoS dataset and a 99.21% accuracy, a 99.02% recall, a 99.05% precision, a 99.10% specificity and a 99.18% f1 score on real-time data. Compared to existing models, the TabNet-SFO model outperformed all existing models in terms of performance metrics and validated its efficiency in detecting attacks.